Multifrequency Channel Characterization for Curved Tunnels

Zhai, Menglin; Zhai, Kai; Cui, Hengrong; Li, Demin

doi:10.1109/lawp.2021.3114553

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…Disadvantage: The training data set needs to be carefully collected as the relationship among the parameters is comparatively complex, and the change in one parameter can affect the change in other parameter output [16], [60], [70], [71], [72].…”

Section: D: Multi-layer Neural Network (Mlnn)mentioning

confidence: 99%

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Wave Propagation Modeling Techniques in Tunnel Environments: A Survey

et al. 2023

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The prediction of radio signal transmission in tunnels is important for wireless communication link design as it enables the delivery of optimum power to the intended receiver inside the tunnel. Several techniques have been proposed to estimate path loss for wireless communication inside tunnels. The radio coverage range and where base stations need to be installed can be figured out with the help of propagation models. At the time of developing these models, modeling complexity and environmental attributes, such as the tunnel geometry and the electrical and magnetic properties of its walls and interior materials are considered. A limited amount of literature currently overviews how waves transmits through tunnels and how electromagnetic wave transmission has recently changed. This survey, which examines current advancements in the propagation of waves in tunnels, aims to address this gap. In this review, thirty wave propagation models were analyzed and grouped into distinct categories for the first time. The detailed specifications of each model were omitted to make the survey easy to comprehend. This comparative research will help service providers adopt an appropriate propagation model and plan efficient transmitter and receiver placement and link management for a specific tunnel environment.INDEX TERMS Artificial neural network, radio wave propagation, ray tracing, scaling, tunnel.

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Section: D: Multi-layer Neural Network (Mlnn)mentioning

confidence: 99%

“…In [71] it was established that the path loss exponent (n) of large-scale models could be accurately presented as (17). 17) where d and f represent arc heights and frequency, respectively.…”

Section: E: Multi-frequency Curved Tunnel (Mfct)mentioning

confidence: 99%

Wave Propagation Modeling Techniques in Tunnel Environments: A Survey

et al. 2023

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“…Combined with waveguide theory and the RT method, [29] uses geometrical optics rules to model the main effects of the tunnel curvature and proposes a heuristic model, which can be useful for preliminary path-loss estimation. More recently, the back propagation neural network is incorporated in the model to predict the path loss in the curved tunnels [30].…”

Section: Improved Fdtd Modelmentioning

confidence: 99%

“…where ε * r represents the complex permittivity of the wall, and φ 1 represents the incident angle of the ray with the vertical wall. In a straight tunnel, φ 1 and N 1 are defined as [30]:…”

Section: The Joint Channel Model Based On Waveguide and Sbrmentioning

confidence: 99%

“…As listed in Table 1, considerable efforts have been devoted to improving the path loss models for different tunnel types during the past few decades [21][22][23][24][25][26][27][28][29][30][31][32][33]. For straight tunnels, the authors of [21] conducted a measurement campaign in subway tunnels at 900/2400 MHz and established a FI model by fitting the measurement results.…”

Section: Introductionmentioning

confidence: 99%

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Path Loss Model for 3.5 GHz and 5.6 GHz Bands in Cascaded Tunnel Environments

Qian

Saleem

et al. 2022

Sensors

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An important and typical scenario of radio propagation in a railway or subway tunnel environment is the cascaded straight and curved tunnel. In this paper, we propose a joint path loss model for cascaded tunnels at 3.5 GHz and 5.6 GHz frequency bands. By combining the waveguide mode theory and the method of shooting and bouncing ray (SBR), it is found that the curvature of tunnels introduces an extra loss in the far-field region, which can be modeled as a linear function of the propagation distance of the signal in the curved tunnel. The channel of the cascaded straight and curved tunnel is thus characterized using the extra loss coefficient (ELC). Based on the ray-tracing (RT) method, an empirical formula between ELC and the radius of the curvature is provided for 3.5 GHz and 5.6 GHz, respectively. Finally, the accuracy of the proposed model is verified by measurement and simulation results. It is shown that the proposed model can predict path loss in cascaded tunnels with desirable accuracy and low complexity.

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Measurements and Modeling of Large-Scale Channel Characteristics in Subway Tunnels at 1.8 and 5.8 GHz

Zhang

Yang

et al. 2023

Antennas Wirel. Propag. Lett.

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Multifrequency Channel Characterization for Curved Tunnels

Cited by 6 publications

References 12 publications

Wave Propagation Modeling Techniques in Tunnel Environments: A Survey

Wave Propagation Modeling Techniques in Tunnel Environments: A Survey

Path Loss Model for 3.5 GHz and 5.6 GHz Bands in Cascaded Tunnel Environments

Measurements and Modeling of Large-Scale Channel Characteristics in Subway Tunnels at 1.8 and 5.8 GHz

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